Weather models are chaotic, are ML methods more numerically stable than a physics based simulation? And how do they compare in terms of compute requirements? the Aurora paper seemed to be promising, but I would love a summary of comparison better than what I get out of Claude.
Once upon a time I converted spectral-transform-shallow-water-model (STSWM or parallelized as PSTSWM) from FORTRAN to Verilog. I believe this is the spectral-transform method we have run for the last 30 years to do forecasting. The forecasting would be ~20% different results for 10-day predictions if we truncated each operation to FP64 instead of Intel's FP80.
Once upon a time I converted spectral-transform-shallow-water-model (STSWM or parallelized as PSTSWM) from FORTRAN to Verilog. I believe this is the spectral-transform method we have run for the last 30 years to do forecasting. The forecasting would be ~20% different results for 10-day predictions if we truncated each operation to FP64 instead of Intel's FP80.